Thursday, August 30, 2012

I've posted about high temperature inducing individuals to exhibit more violent behavior when driving, playing baseballand prowling bars. These cases are neat anecdotes that let us see the "pure aggression" response in lab-like conditions. But they don't affect most of us too much. But violent crime in the real world affects everyone. Earlier, I posted a paper by Jacob et al. that looked at assault in the USA for about a decade - they found that higher temperatures lead to more assault and that the rise in violent crimes rose more quickly than the analogous rise in non-violent property-crime, an indicator that there is a "pure aggression" component to the rise in violent crime.

A new working paper "Crime, Weather, and Climate Change" by recent Harvard grad Matthew Ranson puts together an impressive data set of all types of crime in USA counties for 50 years. The results tell the aggression story using street-level data very clearly:

Note that all crime increases as temperatures rise from 0 F to about 50 F. It seems reasonable to hypothesize that a lot of this pattern comes from "logistical constraints", eg. it's hard to steal a car when it's covered in snow. But above 60 F, only the violent crimes continue to go up: murder, rape, and assault. The comparison between murder and manslaughter is elegantly telling, as manslaughter should be less motivated by malicious intent.

Ranson goes on to make projections about the expected effect of climate change:

Between 2010 and 2099, climate change will cause an additional 30,000 murders, 200,000 cases of rape, 1.4 million aggravated assaults, 2.2 million simple assaults, 400,000 robberies, 3.2 million burglaries, 3.0 million cases of larceny, and 1.3 million cases of vehicle theft in the United States.

This is pretty serious stuff. Ranson also shows that these effects haven't changed much over time, so the prospects for adaptation may be low. And there's no reason to believe that this relationship, which is probably neuro-physiological, doesn't hold outside of the USA.

Tuesday, August 28, 2012

People in agriculture often talk about “weather getting more
variable.” It’s usually hard to know exactly what they mean – sometimes they
are talking about precipitation becoming less frequent and more intense, and
sometimes they're talking about hot extremes becoming more frequent. But it’s
well known that what was considered “extreme” historically can become more
frequent just by shifting the mean of the weather distribution, without any
change in variance. The IPCC SREX figure below shows that clearly in the first
panel.

We care about variance, though, not just because of its
ability to increase the occurrence of historical hot extremes. If variance is
increasing, this would mean more uncertainty faced each year by farmers and
markets about what the growing season weather will be. Note that we are talking
here strictly about weather variance. The variance in production can increase
just from a shift in the distribution towards less favorable temperatures, as
we showed in a recent study led by Dan Urban.

The question of whether variance per se has been changing
(or is projected to change) has received much less attention than whether
extremes are becoming more common. This is partly because changes in variance
are harder to measure than shifts in means or increases in extreme events. But an
interesting analysis by Donat and Alexander in GRL sheds some light directly on
the variance question. They looked at the distribution of daily temperature
anomalies for two 30-year time periods: 1951-1980 and 1981-2010. The figure
below from their paper maps the change between the two time periods for three
parameters of the distribution (mean, variance, and skewness), both for minimum
(left) and maximum (right) temperature.

Two things seem new to me here. (Certainly the shifts in
mean are not new, but it’s interesting to note that the shifts are about equal
for minimum and maximum temperature.) First, the variance changes are mixed
around the world, and not statistically significant in most places (the
significant areas at 10% are shown with hatching). The authors also say that
the variance changes depend a lot on what criteria they use to exclude grid
cells without enough data.

The second interesting thing is that the skewness has
increased in most parts, much more uniformly than the changes in variance. Just
to be clear, we are talking about skewness in the statistical sense, not in the
way it is sometimes used to mean “distorted” or “biased”. An increase in
skewness means that the distribution is now less left-skewed or more
right-skewed than before, which would mean that for a given average, there is a
higher chance of having warm anomalies and a lower chance of having cool
anomalies (see bottom panel of ipcc figure above).

It’s hard to know what exactly is driving the skewness, but
I suspect their paper will spur some more focus on this issue. Maybe it has to do with the shift in rainfall distribution toward heavier events, with less rainfall during moderate events. For now it seems
safe to say that temperatures are not clearly becoming more variable for most
parts of the world, but they seem to be slightly more skewed toward hotness.

Monday, August 27, 2012

Brad Plumber over at Ezra Klein's Wonkblog writes my thoughts better than I do :-)

While my comments about CAFO's in my post
the other day are sure to offend many, I did try to choose my words
carefully. There are many ethical and environmental issues that
surround CAFOs, and I'm not dismissing those issues.

But
we should be aware of indirect consequences of CAFOs. Some of those
indirect consequences can be good for feeding the world and even good
for the environment. It's only responsible to spell out all of those
tradeoffs, and I see that as my job. There are good arguments to be made
that modern industrial agriculture is good for the environment in much
the same way as high-density urban living is good for the environment:
by concentrating these activities we leave less of a footprint on the
planet as a whole.

Incidentally, unlike the other guy in the news these days, I think "wonk" suits Wonkblog very well.

“It’s hard even for people like me to believe, to see that climate change
is actually doing what our worst fears dictated,” said Jennifer A.
Francis, a Rutgers University scientist who studies the effect of sea
ice on weather patterns. “It’s starting to give me chills, to tell you
the truth.”....

....“It’s an example of how uncertainty is not our friend when it comes to
climate-change risk,” said Michael E. Mann, a climate scientist at
Pennsylvania State University. “In this case, the models were almost
certainly too conservative in the changes they were projecting, probably
because of important missing physics.”

Saturday, August 25, 2012

In my OpEd
last week I had a lot of back and fourth with the editor. I probably
had too many statistics and my first draft was just too long. I also
should have provided background information up front for the statistics I
wanted to present.

One thing that got dropped in the
process was an explanation for why retail food prices will rise so
little even though corn prices have increased 60 percent. So much of
our food is ultimately derived from corn, or from other commodities like
wheat and soybeans whose prices track corn prices fairly closely. But
it still makes little difference.

Take meat, for
example. There are only 3-5 pounds of corn used to make an additional
pound of beef, and between 2 and 3 pounds of corn for a pound of chicken
or pork. The calculation
isn't particularly straightforward, but these numbers are probably
about right ``on the margin," as economists like to say. This can vary a
bit from operation to operation or how it's measured, but feed use
efficiency has risen a lot over the last couple decades with the growth of confined animal feeding operations, or CAFOs.

Let's
says 5 pounds of corn per pound of meat. There are 56 pounds of corn
in a bushel and since June prices have increased from about $5 to about
$8 per bushel. This means the amount corn in your quarter-pound burger
have increased from about 11 cents to about 18 cents. If there is
market power by processing companies or retailers, retail prices would
go up by less than this amount (this is basic microeconomics, but I'll
save the details for another time). So, you'll have to squint to see
the effect of this year's drought on prices at grocery stores and
restaurants.

There are lots of complaints about CAFOs
being inhumane for animals. That may be, but they are also extremely
efficient at using resources. Without CAFOs, you would see bigger
prices in all kinds of food, and this year's heat and drought would have
caused a larger price spike. We would also be using more land in crop
production globally, and be using more fertilizers that pollute water
and all manner of other environmental problems that follow from crop
production. Many environmentalists don't like CAFO's but they may well
be doing more good for the environment than eating grass-fed beef,
unless the high price of grass fed beef causes you to eat less.
(Granted, grass-fed beef is probably healthier.)

Anyhow,
the main point is that commodities are a tiny share of retail prices in
developed economies. Prices of most everything, including food, is
made up primarily of labor and capital costs, plus rents to producers
and retailers with market power. The big concern for high commodity
prices in the developed world where the commodity share of food
expenditures is much, much greater and people spend a much larger share
of their income on food.
(cross posted at GGG)

Tuesday, August 21, 2012

Anomalous dry and hot conditions across the US, as well as recent well-publicized research, have amplified the discussion over how human affairs might be affected by changing patters of exposure to extreme heat. You hear the word "non-linear" thrown around a lot in these discussions, so we wanted to clarify some of the things we know (and don't know) about non-linearity in this setting.

For the time-constrained or otherwise impatient, here are the take-homes:

1. Small increases in average temperature can have large effects on extreme heat exposure - so extreme heat exposure tends to increase non-linearly with average temperature.

2. Many economic outcomes we care about (agriculture, other types of economic output) respond negatively to increased exposure to extreme heat, although there is limited evidence that this response is itself non-linear.

3. Because these economic outcomes respond to extreme heat, and extreme heat responds non-linearly to increases in average temperature, then it follows that these outcomes can respond non-linearly to increases in average temperature.

Boiled down even further: extreme heat is bad under current climate, a few degrees of extra warming will increase exposure to extreme heat dramatically in many places, and this could have very large negative impacts on outcomes that folks care about.

Now here's what some of the science says about these points.

1. Extreme heat exposure tends to increase non-linearly with average temperature. This can be pretty easily visualized by looking at some counties in the US. I pulled data for three corn-growing counties in the US (Sioux County in Iowa, Calhoun County in Georgia, and Clay County in Minnesota), and plotted how exposure to extreme heat would change if temperatures increased anywhere between +1C and +7C. Extreme heat as defined here is uses the agronomic notion of "growing degree days", and essentially measures the amount of time an area is exposed to temperatures above a given threshold, set here to 29C. As discussed below, temperatures above 29C have been shown to be particularly harmful for agricultural productivity.

The left panel in each plot shows how much time corn grown in each county spends at different temperatures. The grey line is the average in current climate (the last three decades), and the grey shaded area is the amount of time spent above the magical 29C threshold. The colors show what happens as you increase average temperatures by +2C, +4C, or +6C. Particularly in the places that start out cooler (Clay County in Minnesota), small initial increases in temperature can lead to big increases in exposure to temperatures above 29C.

How much this exposure increases is quantified in the right panel of each plot. The dark line shows the increase in exposure to temperatures above 29C as average temperatures rise up to +7C. The dashed line shows the increase in exposure if every degree of warming was like the first degree of warming - a simple way of looking a looking at linearity. That these lines diverge shows the non-linearity, and as you can read off the right axis, small increases in average temperature mean very large percentage increase in exposure to extreme heat. A +1C warming means anywhere between a 43% (Georgia) and a 61% (Minnesota) increase in exposure to extreme heat under this definition.

2. Many outcomes we care about respond very negatively to extreme heat. This is a very active topic of research, and the other venerable posters on this blog are at the forefront of research on this topic, but let me briefly summarize some findings to date.

Effects of extreme heat on agricultural outcomes are now increasingly well documented. Here's a summary plot from Wolfram and Michael's 2009 PNAS piece, showing how the main US field crops respond to hot temperatures. For both corn and soy, things fall off pretty dramatically above about 29C.

Importantly, though, this is not showing a non-linear response to extreme heat. The paper shows that modeling the yield response to extreme heat linearly does just fine. But this response is still really negative: an extra day spent above 29C (relative to spending it at around 29C, where corn and soy are happy) reduces end of season yield by about 1%. This is huge effect.

David Lobell and colleagues tell a very similar story looking at maize in Africa: yields respond roughly linearly to extreme heat, and the effect size is almost exactly what Wolfram and Michael find in the US.

New findings outside agriculture are eerily similar. Sol shows in a 2010 PNAS paper that non-agricultural economic output in the Caribbean also drops off quickly above 29-30C, and a recent paper by Graff Zivin and Niedell shows a similar dropoff of US labor supply above 29C in industries particularly exposed to climate (construction, agriculture, mining, transportation, etc). Sol has a nice blog post on this.

Again, none of these studies appear to document non-linear responses to extreme heat. Instead, the take-home from all of them is that outcomes respond very negatively (if perhaps linearly) to increased exposure to extreme heat.

3. Outcomes do appear to respond non-linearly to increases in average temperature. This is just a logical extension of the above two points. If extreme heat increases nonlinearly with increases in average temperature, and outcomes respond strongly to changes in extreme heat, then these same outcomes will respond non-linearly to changes in average temperature.

The appropriate adjective to affix to "non-linear" in this setting ("mildly"? "highly"?) is perhaps in the eyes of the beholder. Here is a plot of how Wolfram and Michael's 2009 paper predicts that corn yields will respond to increasing amounts of average warming (I just grabbed the coefficients and standard errors from their appendix Table A5):

The dotted grey line again shows the impact trajectory if every additional degree of warming was like the first +1C of warming - the poor man's linearity. The actual predicted impacts are well below this line starting at about +2C, showing that the responsiveness of US corn yields to temperatures is [fill in preferred adjective] non-linear. I'd imagine the other findings in (2) above would look about the same.

So: increases in average temperature lead to non-linear increases in extreme heat, which will do bad things to outcomes we care about. There isn't a lot of evidence that these outcomes respond non-linearly to extreme heat, but it's not clear how much this matters - a strong negative linear response of these outcomes to extreme heat is enough to generate pretty negative impact projections under future warming.

A final word of caution is probably in order. These empirical studies do a good job of capturing responses to extremes that we've seen in the past. Unavoidably, they do less good of a job imagining how outcomes might respond to future extremes that we haven't yet experienced. It could be that outcomes indeed respond non-linearly to these changes in extremes, instead of responding non-linearly just to changes in averages. If so, this will probably only make a bad story worse.

Friday, August 17, 2012

I am generally a stickler for the peer review process. As a
scientist I know the peer review process isn’t perfect, but it is a very
effective way of weeding out nonsense. And on the topic of food, there is no
shortage of nonsense out there.

These are all complex topics (which I’m guessing is what
drew the Institute’s attention in the first place), and quantitative analysis
can be very difficult -- the type of situation where reviews by peers can be
especially useful. But they clearly have a different mode of doing science than
I do, since they appear to not use the peer review process at all. Instead,
they self issue reports, and do press releases on these reports that get wide
media coverage. It’s possible some
versions of these reports are in peer-review somewhere, but I don’t see any
mention of it on their website.

As I said, the peer review process isn’t perfect. It does
not always help, and it is almost always slow. So I can understand reluctance
to use it, especially when working on such topical issues. But it raises the
question of how credible their work is. For example, I have gotten several
questions from colleagues asking what I think of their work, questions that
likely would not have occurred if the work had been peer reviewed.

But in the case of NECSI, I think they have come up with a
pretty satisfying solution – making testable predictions about the next year. For
example, the figure below is from their most recent report, claiming that the
drought should drive FAO’s food price index to about 240 by the end of the
year. And they are already on record as saying these levels of food prices lead
to large scale social unrest (they state a threshold of 210, so 240 is
actually well above that).

So by the end of the year, surely before most peer review
processes would have been completed, they will have a clear test of their
model. Now, it’s possible they could get things right for the wrong reasons – a
broken clock is right twice a day. But they are going out on a limb, which is a
way to establish credibility. Hopefully, they will be as honest and diligent in
reporting their failures as their successes.

Monday, August 13, 2012

Extreme heat and droughts -- a recipe for world food woes

With extreme heat and the worst drought in half a century continuing
to plague the farm states, there are important lessons to be learned for
all of us -- farmers, consumers and the world's poorest populations
alike -- about the effect of climate change.

The Agriculture
Department announced this season's first major crop yield forecasts, and
they weren't pretty: a nationwide average of 123.4 bushels of corn per
acre, the lowest level since 1995. Soybean yield is expected to be low
too, though not as bad as corn.

The United States, which
is the world's largest producer and exporter of staple grains, is
grappling with the biggest surprise in production shortfalls since the
Dust Bowl of the 1930s. Certainly, this July surpassed July 1936 as the hottest month on record.

Friday, August 10, 2012

USDA today announced its forecast for corn yields. It might be fun to compare those forecast to one using a statistical model of corn yields that my colleague Michael Roberts and I have developed. It uses only four temperature variables (two temperature and two precipitation variables - if you want to read more, here's a link to the paper). The temperature variables in 2012 are shown here.

All weather variables in the model are season totals for March 1st - August 31st. The following graph combines actual weather observations for March 1st-August 6, 2012 with historic averages for August 7th-August 31st in each county. Once the actual weather for the rest of August is realized, the predictions will obviously change dependent on whether it warmer or cooler than usual.

The eastern counties in the graph account for 85% of the corn that is grown in the US. While some areas areas are indeed hit very hard (-80 log points is a 55% decline in yields), some areas in the south and northern edge should actually have above normal yields. Overall production in this area is predicted to decline 14% compared to the trend, which is much less severe than what USDA is saying.

Following up on an early post about the record setting heat in the United States, below are a few more plots to show the spatial distribution of the heat wave. The weather data has been updated to August 6, 2012. Here is the overall US average (red line is 2012, the grey lines are 1960-2011) for degree days above 29C, the weather variable that best predicts corn yields.

There is considerable spatial heterogeneity in how hot it has been. The next graph shows anomalies (difference to the 1950-2011 historic average) for degree days above 29C for counties in the Eastern United State. The data uses March 1st - August 6th, 2012. For comparison, the historic US average for the entire season (March 1st-August 31) is 34 degree days, so an extra 135 is four times the historic average - and that is on top of the historic average in a given location!

There is even more heterogeneity for rainfall. While places along the Mississippi River seem dry, some Northern and Southern counties actually had above normal rainfall.

USDA’s monthly report is out today. A lot of attention is
going to the new corn estimates, which put forecasted yields at 123 bu/acre.
Trend yield for 2012 is about 160 bu/acre, so that would mean a 23% drop from
trend. That’s still not quite as bad as 1988, which was closer to 30% belowtrend. As Wolfram showed in a previous post, the heat this year has been about
as bad as ever, the rainfall not quite as bad as 1988. So overall I don’t think
the downward revisions by USDA should come as much of a surprise.

What I hadn’t been paying as much attention to was the
situation in other crops. Lost in the news was that USDA actually downgraded
the forecast of global production not just for corn and soy, but also wheat and
rice. Wheat downgrades are mainly related to the former soviet union, with
Russia and Kazakhstan seeing “July heat and dryness across most of the spring
wheat growing areas.” For rice, there have been lots of stories about the late
monsoon in India, although conditions seem to be improving there a lot in the
last week.

Overall, the production forecasts for wheat, rice, and
coarse grains are all lower than what production was last year. This is not so
unusual in a historical sense. For example, I plot below the global production for these three since
1961 (all points up to 2010 are from FAO, last 2 are estimates/forecasts from the
latest USDA numbers). Gray lines show years where production of all three was
down from previous year. Since 1961 there have been 7 other years where all
three crops dropped, including three since 2000.

Even if it’s not unusual, it’s a little surprising to me
that it would occur in a year that had such high prices to begin with. A lot of
economists argue that yields are very price responsive, for example farmers will
put more fertilizer or labor into a crop if prices are high. Others say that
yields and production are not very responsive in the short term, but over the
long term production will respond (mainly because of expanding area). I’m not
sure yet what to make of the recent data, but it certainly seems like a good
test of theory. Hopefully somebody out there is calculating what production
changes over the past 3-4 seasons, when prices have been high, can tell us
about the likely value of supply elasticity.

Friday, August 3, 2012

David Lobell mentioned that there seemed to be less news coverage of the drought, so I checked Google Trends and David was right. Looking just the USA, interest in the drought peaked about a week ago:

(news report volume looks similar, but Google doesn't give me the raw data). Is interest/news falling because the nation's corn crop has recovered? Probably not. But a week ago, something else took over the airwaves and peoples' attention:

Is this spurious? It's possible, but this general pattern is well documented. In a 2007 article, David Strömberg linked the quantity of US disaster relief (a proxy for public interest) to "whether the disaster occurs at the same time as other newsworthy events, such as the Olympic Games, which are obviously unrelated to need." He concludes "that the only plausible explanation of this is that relief decisions are driven by news coverage of disasters and that the other newsworthy material crowds out this news coverage." So it isn't crazy to think that the London Games might soak up some of the public interest that would otherwise go towards our own drought.

Yet, while it seems unlucky for folks in the midwest to get hit by this drought during the Olympics, they are "lucky enough" to get hit just before the presidential race. In their 2007 paper, Thomas Garrett and Russell Sobel"find that presidential and congressional influences affect the rate of disaster declaration and the allocation of FEMA disaster expenditures across states. States politically important to the president have a higher rate of disaster declaration by the president... Election year impacts are also found. Our models predict that nearly half of all disaster relief is motivated politically rather than by need.The findings reject a purely altruistic model of FEMA assistance and question the relative effectiveness of government versus private disaster relief."

Thursday, August 2, 2012

There seems to be a big push to roll back the ethanol mandate, at
least temporarily, due to the crop losses and high prices for corn,
soybeans and wheat we're experiencing this year. See, for example,
Colin Carter and Henry Miller's Op Ed in the New York Times.

How much would a temporary suspension of the mandate affect prices?

As I write, the future price for corn delivered in December 2012
is $7.95/bu. The price for delivery in December 2013 is just $6.30.
So, there is no incentive to store commodities, and inventories are very
low. So, any reprieve on the demand side will push directly on this
year's price. With regard to prices, it would be equivalent to reducing
the size of crop losses. If we lose 1/3 of the crop from heat and
drought, and we reduce demand by 1/3 by temporarily halting ethanol
production, we'd probably go back to early-Spring prices of around
$4-5/bu.

One problem with this back-of-the-envelope
calculation is that ethanol production is unlikely to stop completely
just due to a temporary suspension of the mandate. There are shutdown
and startup costs, and a 10% ethanol blend is firmly in place.
So prices probably wouldn't fall back that far, but they would fall a
lot.

In fact, I wouldn't be surprised if
speculation about a temporary
suspension of the mandate is already folded into
futures prices, at least partly. It's hard to know what the odds of a
repeal might be, but the market knows it's not zero. And
the worse are crop losses, the greater the odds of a temporary
suspension.

What's more subtle and potentially more interesting is
that temporarily repealing the mandate would set a precedent that would
affect futures prices and inventory demand going forward. It would be
interesting to evaluate an ethanol policy with a "safety valve" that
would relax the mandate in the event prices exceeded some threshold.
This kind of analysis is more difficult. Nam Tran, a grad student at
NCSU, is working on it. I'll post his results here if and when he has
them.